Journal ArticleDOI
A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm
TLDR
A fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm yields sufficient and sensible imputation performance results.About:
This article is published in Information Sciences.The article was published on 2013-06-01. It has received 256 citations till now. The article focuses on the topics: Imputation (statistics) & Fuzzy clustering.read more
Citations
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Journal ArticleDOI
Missing value imputation: a review and analysis of the literature (2006–2017)
TL;DR: This paper aims at reviewing and analyzing related studies carried out in recent decades, from the experimental design perspective, and identifying limitations in the existing body of literature based upon which some directions for future research can be gleaned.
Book ChapterDOI
Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014
TL;DR: A comprehensive survey on FCM and its applications in more than one decade has been carried out to show the efficiency and applicability in a mixture of domains and to encourage new researchers to make use of this simple algorithm.
Journal ArticleDOI
Artificial algae algorithm (AAA) for nonlinear global optimization
TL;DR: This study proposes a novel bio-inspired metaheuristic optimization algorithm called artificial algae algorithm (AAA) inspired by the living behaviors of microalgae, photosynthetic species and shows that it is a balanced and consistent algorithm.
Journal ArticleDOI
Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
TL;DR: A systematic review of various state-of-the-art data preprocessing tricks as well as robust principal component analysis methods for process understanding and monitoring applications and big data perspectives on potential challenges and opportunities have been highlighted.
Posted ContentDOI
A survey on missing data in machine learning.
Tlamelo Emmanuel,Thabiso M. Maupong,Dimane Mpoeleng,Thabo Semong,Banyatsang Mphago,Oteng Tabona +5 more
TL;DR: This paper aggregates some of the literature on missing data particularly focusing on machine learning techniques, and gives insight on how the machine learning approaches work by highlighting the key features of the proposed techniques, how they perform, their limitations and the kind of data they are most suitable for.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.